Probabilistic weighted Dirichlet process mixture with an application to stochastic volatility models

IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY
Peng Sun, Inyoung Kim, Ki-Ahm Lee
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引用次数: 0

Abstract

In this article, we propose a flexible Bayesian modelling framework and investigate the probabilistic weighted Dirichlet process mixture (pWDPM). The construction and properties of a probabilistic weight function are illustrated. The advantage of the pWDPM under the log-squared transformed stochastic volatility (SV) model is demonstrated. We achieve greater modelling flexibility by relaxing the distributional assumption of the error term. Bayesian inference for the pWDPM under SV and sampling procedures are provided. The performance of the pWDPM is evaluated using simulation studies and empirical results. Both computational efficiency and model accuracy are achieved through the pWDPM.

概率加权狄利克雷混合过程及其在随机波动模型中的应用
在本文中,我们提出了一个灵活的贝叶斯建模框架,并研究了概率加权狄利克雷过程混合(pWDPM)。说明了概率权函数的构造和性质。验证了pWDPM在对数平方变换随机波动率(SV)模型下的优势。我们通过放宽误差项的分布假设来实现更大的建模灵活性。给出了SV下pWDPM的贝叶斯推断和抽样过程。利用仿真研究和实证结果对pWDPM的性能进行了评价。该方法既提高了计算效率,又提高了模型精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
1.40
自引率
0.00%
发文量
62
审稿时长
>12 weeks
期刊介绍: The Canadian Journal of Statistics is the official journal of the Statistical Society of Canada. It has a reputation internationally as an excellent journal. The editorial board is comprised of statistical scientists with applied, computational, methodological, theoretical and probabilistic interests. Their role is to ensure that the journal continues to provide an international forum for the discipline of Statistics. The journal seeks papers making broad points of interest to many readers, whereas papers making important points of more specific interest are better placed in more specialized journals. The levels of innovation and impact are key in the evaluation of submitted manuscripts.
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